CN109886067A - Wetland is damaged remote sensing recognition method and device - Google Patents
Wetland is damaged remote sensing recognition method and device Download PDFInfo
- Publication number
- CN109886067A CN109886067A CN201811543634.6A CN201811543634A CN109886067A CN 109886067 A CN109886067 A CN 109886067A CN 201811543634 A CN201811543634 A CN 201811543634A CN 109886067 A CN109886067 A CN 109886067A
- Authority
- CN
- China
- Prior art keywords
- wetland
- subregion
- sub
- period
- impaired
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the present invention provides a kind of impaired remote sensing recognition method and device of wetland, the described method includes: obtaining the land use data of the first default historical time section region of interest within, the impaired spatial distribution and impaired type of Wetland Type data, wetland is obtained according to land use data, according to Wetland Type data acquisition maximum wetland range, maximum wetland range is divided into multiple subregions；Obtain the image data of all subregion in the second default historical time section, second default historical time section is divided into multiple sub- periods, according to the image data of all subregion in each sub- period, multiple wetland elements of all subregion in each sub- period are obtained, and calculate each wetland element extent of damage of all subregion in each sub- period；According to the maximum wetland element of all subregion extent of damage, wetland in each sub- period impaired spatial distribution and impaired type, the impaired mode of all subregion is determined.The embodiment of the present invention improves the accuracy and accuracy of the impaired identification of wetland.
Description
Technical field
The embodiment of the present invention belongs to environmental monitoring technology field, is damaged remote sensing recognition side more particularly, to a kind of wetland
Method and device.
Background technique
Wetland is one of productivity and the highest ecosystem of bio-diversity on the earth, is claimed together with forest and ocean
For the three big ecosystems.Wetland is known as " kidney of the earth " because of its huge environmental functional and environmental benefit, resist flood,
Regulated flow improves weather, purifies water and local area ecological balance aspect is maintained to play an important role.Due to by the mankind
Movable influence, the whole world have the wetland of half to degenerate.Therefore how to carry out the impaired identification work of wetland, to the extensive of wetland
Multiple, reconstruction and reasonable development have important ecological significance.
In recent years, the major technique that remote sensing is increasingly becoming monitoring wetland reserves, extracts the Wetlands Monitorings such as wetland information.From most
To traditional supervised classification, the machine learning methods such as artificial neural network developed till now obtain wet first visual interpretation method
Ground type and area study the variation of Wetland Type and landscape pattern accordingly.There is research and utilization visual interpretation method, is based on
Landsat satellite image is classified extraction to the wetland in the whole nation, and the variation of wetland is monitored and analyzed.Due to
The elements such as water body, vegetation and soil play a significant role in terms of maintaining wetland health and ecological functions in wetland, are based on wetland
The research of element also joined in the impaired research of wetland.By utilize multi- source Remote Sensing Data data, using water body index, vegetation index and
Spectrum solution mixes the damage situations of technical research wetland element.There are the satellite remote sensing images of research and utilization MODIS long-term sequence, leads to
Cross vegetation, water and vegetation-water composite index potentiality monitoring wetlands ecosystems variation.
In Wetland Type variation monitoring, the above method is simple to operation, in the impaired identification of a wide range of wetland, Ke Yifa
Preferable effect is waved, but often focuses on the variation of wetland between different type, and has omited the mutation analysis of wetland of the same race, while wet
Ground element be damaged Study of recognition based on the single index study such as wetland water, wetland soil, wetland ecotourism, so as to cause wetland by
It is inaccurate to damage recognition result.
Summary of the invention
To overcome the problems, such as that it is inaccurate or at least partly that above-mentioned existing wetland is damaged remote sensing recognition method recognition result
Ground solves the above problems, and the embodiment of the present invention provides a kind of impaired remote sensing recognition method and device of wetland.
According to a first aspect of the embodiments of the present invention, a kind of impaired remote sensing recognition method of wetland is provided, comprising:
The land use data for obtaining the first default historical time section region of interest within, when according to the first default history
Between land use data in section obtain the impaired spatial distribution and impaired type of Wetland Type data, wetland, according to described wet
Ground categorical data obtains the maximum wetland range in the target area, and the maximum wetland range is divided into multiple sub-districts
Domain；
The image data for obtaining each subregion in the second default historical time section, by the described second default historical time
Section is divided into multiple sub- periods, according to the image data of all subregion in each sub- period, obtains each sub- time
Multiple wetland elements of all subregion in section, and each wetland element for calculating all subregion in each sub- period is damaged journey
Degree；
According to the impaired space of the maximum wetland element of all subregion extent of damage, the wetland in each sub- period
Distribution and impaired type, determine the impaired mode of each subregion.
Second aspect according to embodiments of the present invention provides a kind of impaired remote sensing recognition device of wetland, comprising:
Module is obtained, for obtaining the land use data of the first default historical time section region of interest within, according to described
Land use data in first default historical time section obtains the impaired spatial distribution and impaired class of Wetland Type data, wetland
Type, according to the maximum wetland range in target area described in the Wetland Type data acquisition, and by the maximum wetland range
It is divided into multiple subregions；
Computing module, for obtaining the image data of each subregion in the second default historical time section, by described
Two default historical time sections are divided into multiple sub- periods, according to the image data of all subregion in each sub- period, obtain
Multiple wetland elements of all subregion in each sub- period are taken, and calculate each wet of all subregion in each sub- period
The ground element extent of damage；
Identification module, for according to the maximum wetland element of all subregion extent of damage in each sub- period, described
Wetland impaired spatial distribution and impaired type, determine the impaired mode of each subregion.
In terms of third according to an embodiment of the present invention, a kind of electronic equipment is also provided, comprising:
At least one processor；And
At least one processor being connect with the processor communication, in which:
The memory is stored with the program instruction that can be executed by the processor, and the processor calls described program to refer to
Order impaired be able to carry out wetland provided by any possible implementation in the various possible implementations of first aspect
Remote sensing recognition method.
4th aspect according to an embodiment of the present invention, also provides a kind of non-transient computer readable storage medium, described
Non-transient computer readable storage medium stores computer instruction, and the computer instruction makes the computer execute first aspect
Various possible implementations in wetland provided by any possible implementation be damaged remote sensing recognition method.
The embodiment of the present invention provides a kind of impaired remote sensing recognition method and device of wetland, and this method passes through Wetland Type first
With the identification of the extent of damage, wetland is obtained in region-wide main affected area, the impaired analysis of wetland element is secondly carried out, obtains
Damage situations of each wetland element within the scope of maximum wetland are taken, wetland damage situations are refined by dividing subregion, in conjunction with wet
Ground element integrates in the damage situations of different times different zones and obtains the impaired mode of wetland, improves the impaired identification of wetland
Accuracy and accuracy, the restoration and reconstruction of wetland are provided fundamental basis for after.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is this hair
Bright some embodiments for those of ordinary skill in the art without creative efforts, can be with root
Other attached drawings are obtained according to these attached drawings.
Fig. 1 is that wetland provided in an embodiment of the present invention is damaged remote sensing recognition method overall flow schematic diagram；
Fig. 2 is that wetland provided in an embodiment of the present invention is damaged remote sensing recognition device overall structure diagram；
Fig. 3 is electronic equipment overall structure diagram provided in an embodiment of the present invention.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art
Every other embodiment obtained without creative efforts, shall fall within the protection scope of the present invention.
A kind of impaired remote sensing recognition method of wetland is provided in one embodiment of the invention, and Fig. 1 is the embodiment of the present invention
The wetland of offer is damaged remote sensing recognition method overall flow schematic diagram, this method comprises: S101, obtains the first default historical time
The land use data of section region of interest within obtains wetland according to the land use data in the described first default historical time section
Categorical data, wetland impaired spatial distribution and impaired type, according in target area described in the Wetland Type data acquisition
Maximum wetland range, and the maximum wetland range is divided into multiple subregions；
Wherein, target area is the region for needing to carry out wetland and being damaged remote sensing recognition.Obtain mesh in default historical time section
The Landsat data for marking region obtain the land use data of target area by man computer interactive interpretation.Land use data packet
Include the land use pattern of each position in soil region.According to the soil of each moment target area in the first default historical time section
Using data, the maximum wetland range in target area is obtained.Wherein, which can be every in the first default historical time section
At the time of one month, Duo Geyue, 1 year or many years.As the time is different, the land use situation of each position in target area
It can change.Using land use transfer matrix, the spatial distribution that wetland is impaired in the different sub- periods is obtained.From target area
Wetland Type data are obtained in the land use data at domain all moment.Wherein, the region of Wetland Type data packet Wetland Type
Position.Wetland Type data in comprehensive all moment target areas, determine the maximum wetland range in target area.According to water
Position and Wetland Type, are divided into multiple subregions for maximum wetland range.
S102 obtains the land use data of the first default historical time section region of interest within, default according to described first
Land use data in historical time section obtains the impaired spatial distribution and impaired type of Wetland Type data, wetland, according to
Maximum wetland range in target area described in the Wetland Type data acquisition, and the maximum wetland range is divided into more
Sub-regions；
The Landsat image data for obtaining target area long-term sequence, therefrom obtains each son in the second preset time period
The image data in region.Second preset time period is divided into multiple sub- periods, such as is divided into three sub- periods.From each son
Wetland element of all subregion in each period is extracted in the image data of region each sub- period, from each in the present embodiment
Multiple wetland elements are extracted simultaneously in each image data of subregion, such as water body, vegetation and soil moisture.According to all subregion
In the wetland element of each sub- period, the extent of damage of each wetland element of all subregion in each sub- period is calculated.
The present embodiment is not limited to the number of sub- period, the number and classification of wetland element.
S103, it is impaired according to the maximum wetland element of all subregion extent of damage, the wetland in each sub- period
Spatial distribution and impaired type, determine the impaired mode of each subregion.
The maximum wetland elements recognition of all subregion extent of damage in each sub- period is come out.According to each sub- time
The maximum wetland element of all subregion extent of damage in section, determines the impaired mode of all subregion.For example, in first sub- time
The water body element extent of damage of section, certain subregion is maximum；In second sub- period, the vegetation element extent of damage of the subregion
It is maximum；In the third sub- period, the vegetation element extent of damage of the subregion is maximum.By any subregion in each sub- period
The middle maximum wetland element of the extent of damage is damaged spatial distribution in conjunction with wetland in different time sections, according to the period of the day from 11 p.m. to 1 a.m in big region
Between the sequence arrangement of time order and function of section be combined, by combination combination progress corresponding with pre-stored impaired mode
Match, so that it is determined that the impaired mode of all subregion.For example, certain sub-regions extent of damage in three sub- periods is maximum wet
Ground factor combination be [water body, vegetation, vegetation], then from the corresponding impaired mode of the pre-stored combination be water, water plant,
Terrestrial plant.
The present embodiment passes through the identification of Wetland Type and the extent of damage first, obtains wetland in region-wide main damaged zone
Secondly domain carries out the damaged process analysis of wetland element, obtains damage situations of each wetland element within the scope of maximum wetland, lead to
Cross division subregion refinement wetland damage situations, in conjunction with wetland element different times different zones damage situations, it is comprehensive
It is damaged mode to wetland, improves accuracy and accuracy that wetland is damaged remote sensing recognition, the restoration and reconstruction of wetland mention for after
For theoretical basis.
On the basis of the above embodiments, in the present embodiment according to the Wetland Type data in the land use data,
The step of obtaining the maximum wetland range in the target area specifically includes: obtaining in the first default historical time section every pre-
If the land use data of duration, according to the land use data, obtains the impaired spatial distribution of wetland and be damaged；
To in each land use data Wetland Type data and the impaired spatial distribution of wetland and impaired type be laid out point
Analysis, obtains the maximum wetland range in the target area.
Specifically, when determining the maximum wetland range in target area, in the first first default historical time section every
The land use data of preset duration, such as every 1 year or one month land use data.It is mentioned from each land use data
Take Wetland Type data.Maximum wetland range is determined by Overlap Analysis.Land use pattern is represented with x, w represents wetland class
Type, k represent the quantity of Wetland Type figure, and S indicates maximum wetland range, then the calculation formula of maximum wetland range are as follows:
By calculation formula as can be seen that if each position is in any soil benefit selected every preset duration in target area
With Wetland Type is belonged in data, then the position belongs to maximum wetland range.
On the basis of the above embodiments, according to the image number of all subregion in each sub- period in the present embodiment
According to the step of multiple elements of all subregion specifically includes in acquisition each sub- period: according in each sub- period
The image data of all subregion extracts the normalization water body index of all subregion in each sub- period respectively, normalization is planted
By index and soil moisture index；The normalization water body index of each subregion is wanted as the water body of each subregion
Element；Using the normalized differential vegetation index of each subregion as the vegetation element of each subregion；By each subregion
Soil moisture element of the soil moisture index as each subregion.
Specifically, according to the Landsat image data of long-term sequence, obtain wetland element index, choose respectively water body,
Three elements of vegetation and soil moisture characterize wetland, and using NDWI, (Normalized Difference Water Index, returns
One change water body index), NDVI (Normalized Difference Vegetation Index, normalized differential vegetation index) and
SMMI (Soil Moisture Monitoring Index, soil moisture index) respectively indicates these three wetland elements.
E=F (NDWI, NDVI, SMMI)
In formula, E represents wetland element, wherein the calculation formula of F function representation wetland element, NDWI, NDVI and SMMI table
Show wetland water body, vegetation and soil moisture element.
The calculation formula of each index are as follows:
Wherein, Green indicates that the green band in the image data, NIR indicate the near-infrared in the image data
Wave band, Red indicate that the red band in the image data, SWIR indicate the short infrared wave band in the image data.
On the basis of the above embodiments, each sub-district in each sub- period is calculated by the following formula in the present embodiment
Each element extent of damage in domain:
Wherein, Z indicates any element saliency value of any subregion in any sub- period, and n indicates any described
The length of sub- period, EiIndicate any subregion 1 year in any sub- period any element value, EjIndicate any
Any element value in any subregion jth year in the sub- period；It is respectively wanted according to all subregion in each sub- period
The S value and Z value of element, determine the wetland element extent of damage of all subregion.
Specifically, within the scope of maximum wetland, each wetland element in all subregion is obtained by trend analysis and is existed
The damage situations of long-term sequence, and damage situations are classified.The range for counting Z value is (- ∞ ,+∞).In conspicuousness water
Flat α, such as α=0.05, if | Z | > 1.96, then it represents that it is significant that α in 0.05 level rises or falls trend.It will be each in each period
The S value and Z value of each element of subregion, preset condition corresponding with each default extent of damage are compared, preset condition such as table 1
Shown, being such as significantly damaged corresponding preset condition is SNDVI>=0.005, SNDWI>=0, SSMMIValue >=1.96 >=0, Z.Appoint if meeting
The corresponding wetland element of the preset condition is then damaged grade as the wetland element of all subregion and was damaged by one preset condition
Journey obtains wetland element impaired trend, period and region.
1 wetland element of table is damaged grade classification table
On the basis of the above embodiments, in the present embodiment according to the Wetland Type data in the land use data,
The step of obtaining the maximum wetland range in the target area further include: according to the beginning of the described first default historical time section
The land use data at moment and finish time constructs the Markov transferring matrix of land use pattern；According to the Ma Er
Section's husband's transfer matrix determines various types of wetlands in the target area；The type includes that can restore wetland and can not be extensive
Multiple wetland；Correspondingly, according to the maximum element of all subregion extent of damage in each sub- period, each subregion is determined
Impaired mode the step of specifically include: according to the maximum element of all subregion extent of damage and each son in each sub- period
The wetland in region is damaged spatial distribution, determines the impaired mode of each subregion.
Specifically, using land use transfer matrix, the Wetland Area change in time and space of initial period and tail end is analyzed.
Wetland after indicating variation with P is damaged distribution map, Pt1And Pt2Respectively indicate initial period beginning and end period target area
Land use data is damaged spatial and temporal distributions by calculating the transfer of different times land use pattern and wetland using product variation,
Formula is as follows:
S=Pt1*10+Pt2*1。
By the variation of Wetland Type, various types of wetlands in target area are determined, including irrecoverable wetland and can
Restore wetland.Wherein, irrecoverable wetland includes the region that construction land etc. is converted by wetland, and can restore wetland includes by wet
Ground is converted into the region in farmland, meadow, forest land etc..According to the maximum element of all subregion extent of damage in each sub- period, knot
It closes wetland in different time sections and is damaged spatial distribution in big region, the impaired mode of all subregion is determined, to pass through wetland
The mutation analysis of type and the damaged process analysis of wetland element, it is comprehensive to obtain wetland damage situations.For example, certain sub-regions exists
The maximum wetland factor combination of the extent of damage is [water body, vegetation, vegetation] in three sub- periods, which is that can restore wet
Ground is water, water plant, terrestrial plant from the corresponding impaired mode of the pre-stored combination, then according to the last one period of the day from 11 p.m. to 1 a.m
Between the corresponding terrestrial plant of section and the subregion known to Wetland Type can be restored be finally translated into farmland.It can not if the subregion is
Restore wetland, then the subregion is finally translated into construction land.
A kind of impaired remote sensing recognition device of wetland is provided in another embodiment of the present invention, and the device is for realizing preceding
State the method in each embodiment.Therefore, the description and definition in each embodiment that aforementioned wetland is damaged remote sensing recognition method, can
With the understanding for execution module each in the embodiment of the present invention.Fig. 2 is that wetland provided in an embodiment of the present invention is damaged remote sensing knowledge
Other device overall structure diagram, the device include obtaining module 201, computing module 202 and identification module 203；Wherein:
The land use data that module 201 is used to obtain the first default historical time section region of interest within is obtained, according to institute
The land use data in the first default historical time section is stated to obtain the impaired spatial distribution of Wetland Type data, wetland and be damaged
Type, according to the maximum wetland range in target area described in the Wetland Type data acquisition, and by the maximum wetland model
It encloses and is divided into multiple subregions；
Wherein, target area is the region for needing to carry out wetland and being damaged remote sensing recognition.Obtain mesh in default historical time section
The Landsat data for marking region obtain the land use data of target area by man computer interactive interpretation.Land use data packet
Include the land use pattern of each position in soil region.According to the soil of each moment target area in the first default historical time section
Using data, the maximum wetland range in target area is obtained.Wherein, which can be every in the first default historical time section
At the time of one month, Duo Geyue, 1 year or many years.As the time is different, the land use situation of each position in target area
It can change.Wetland Type data are obtained from the land use data at target area all moment.Wherein, Wetland Type number
According to the regional location of packet Wetland Type.The Wetland Type data in the comprehensive all moment target areas of module 201 are obtained, determine mesh
Mark the maximum wetland range in region.According to water level and Wetland Type, maximum wetland range is divided into multiple subregions.
Computing module 202 is used to obtain the image data of each subregion in the second default historical time section, will be described
Second default historical time section is divided into multiple sub- periods, according to the image data of all subregion in each sub- period,
Multiple wetland elements of all subregion in each sub- period are obtained, and calculate each of all subregion in each sub- period
The wetland element extent of damage；
The Landsat image data for obtaining target area long-term sequence, therefrom obtains each son in the second preset time period
The image data in region.Second preset time period is divided into multiple sub- periods.Computing module 202 is from the every height of all subregion
Wetland element of all subregion in each period is extracted in the image data of period, from each of all subregion in the present embodiment
It opens and extracts multiple wetland elements in image data simultaneously.Wetland element according to all subregion in each sub- period calculates each
The extent of damage of each wetland element of subregion in each sub- period.The present embodiment is not limited to the number, wet of sub- period
The number and classification of ground element.
Identification module 203 is used for according to the maximum wetland element of all subregion extent of damage, institute in each sub- period
Wetland impaired spatial distribution and impaired type are stated, determines the impaired mode of each subregion.
Identification module 203 comes out the maximum wetland elements recognition of all subregion extent of damage in each sub- period.
According to the maximum wetland element of all subregion extent of damage in each sub- period, the impaired mode of all subregion is determined.It will be any
Subregion maximum wetland element of the extent of damage in each sub- period, in conjunction with the spatial distribution that wetland is damaged, according to the sub- time
The sequence arrangement of the time order and function of section is combined, by combination combination progress corresponding with pre-stored impaired mode
Match, so that it is determined that the impaired mode of all subregion.
The present embodiment passes through the identification of Wetland Type and the extent of damage first, obtains wetland in region-wide main damaged zone
Secondly domain carries out the impaired analysis of wetland element, obtains damage situations of each wetland element within the scope of maximum wetland, by drawing
Molecular domains refine wetland damage situations, and in conjunction with wetland element in the damage situations of different times different zones, synthesis obtains wet
Ground is damaged mode, improves the accuracy and accuracy of the impaired identification of wetland, the restoration and reconstruction of wetland provide theoretical base for after
Plinth.
On the basis of the above embodiments, module is obtained in the present embodiment to be specifically used for: obtaining the first default historical time
Every the land use data of preset duration in section；Wetland Type data in each land use data are folded
Analysis is set, the maximum wetland range in the target area is obtained.
On the basis of the above embodiments, computing module is further used in the present embodiment: according to each sub- period
The image data of middle all subregion extracts the normalization water body index of all subregion in each sub- period, normalization respectively
Vegetation index and soil moisture index；The normalization water body index of each subregion is wanted as the water body of each subregion
Element；Using the normalized differential vegetation index of each subregion as the vegetation element of each subregion；By each subregion
Soil moisture element of the soil moisture index as each subregion.
On the basis of the above embodiments, computing module is calculated by the following formula each sub- period in the present embodiment
The normalization water body index NDWI of middle all subregion:
It is calculated by the following formula the normalized differential vegetation index NDVI of all subregion in each sub- period:
It is calculated by the following formula the soil moisture index SMMI of all subregion in each sub- period:
Wherein, Green indicates that the green band in the image data, NIR indicate the near-infrared in the image data
Wave band, Red indicate that the red band in the image data, SWIR indicate the short infrared wave band in the image data.
On the basis of the above embodiments, computing module is calculated by the following formula each sub- period in the present embodiment
Each wetland element extent of damage of middle all subregion:
Wherein, Z indicates any wetland element saliency value of any subregion in any sub- period, and n indicates any
The length of the sub- period, EiIndicate any subregion 1 year in any sub- period any wetland element value, Ej
Indicate any wetland element value in any subregion jth year in any sub- period；According to each in each sub- period
The S value and Z value of each wetland element of subregion determine that each wetland element of all subregion in each sub- period is damaged journey
Degree.
On the basis of the above embodiments, computing module is further used in the present embodiment: will be each in each period
The S value and Z value of each wetland element of subregion, preset condition corresponding with each default extent of damage are compared；It will be each described
The corresponding default extent of damage conduct of the preset condition that the S value of each wetland element of all subregion and Z value are met in period
Each wetland element extent of damage of each subregion.
On the basis of the above embodiments, module is obtained in the present embodiment to be also used to: obtaining the first default historical time section
The land use data of region of interest within obtains the impaired spatial distribution of Wetland Type data, wetland according to land use data
Maximum wetland range is divided by multiple subregions according to Wetland Type data acquisition maximum wetland range with impaired type；It obtains
Second default historical time section is divided into multiple sub- times by the image data for taking all subregion in the second default historical time section
Section obtains multiple wetland elements of all subregion in each sub- period according to the image data of all subregion in each sub- period,
And calculate each wetland element extent of damage of all subregion in each sub- period；Journey is damaged according to all subregion in each sub- period
Maximum wetland element, wetland impaired spatial distribution and impaired type are spent, determines the impaired mode of all subregion.
The present embodiment provides a kind of electronic equipment, Fig. 3 is electronic equipment overall structure provided in an embodiment of the present invention signal
Figure, which includes: at least one processor 301, at least one processor 302 and bus 303；Wherein,
Processor 301 and memory 302 pass through bus 303 and complete mutual communication；
Memory 302 is stored with the program instruction that can be executed by processor 301, and the instruction of processor caller is able to carry out
Method provided by above-mentioned each method embodiment, for example, obtain the soil of the first default historical time section region of interest within
Using data, the impaired spatial distribution and impaired type of Wetland Type data, wetland is obtained according to land use data, according to wet
Ground categorical data obtains maximum wetland range, and maximum wetland range is divided into multiple subregions；When obtaining the second default history
Between in section all subregion image data, the second default historical time section is divided into multiple sub- periods, according to each sub- time
The image data of all subregion in section, obtains multiple wetland elements of all subregion in each sub- period, and calculates each sub- time
Each wetland element extent of damage of all subregion in section；It is wanted according to the maximum wetland of all subregion extent of damage in each sub- period
Element, wetland impaired spatial distribution and impaired type, determine the impaired mode of all subregion.
The present embodiment provides a kind of non-transient computer readable storage medium, non-transient computer readable storage medium storages
Computer instruction, computer instruction make computer execute method provided by above-mentioned each method embodiment, for example, obtain the
The land use data and wetland of one default historical time section region of interest within are damaged spatial distribution, are obtained according to land use data
Wetland Type data are taken, and obtain the maximum wetland range in target area, and maximum wetland range is divided into multiple sub-districts
Domain；Second default historical time section is divided into multiple by the image data for obtaining all subregion in the second default historical time section
The sub- period, according to the image data of all subregion in each sub- period, all subregion is multiple wet in acquisition each sub- period
Ground element, and calculate each wetland element extent of damage of all subregion in each sub- period；According to each sub-district in each sub- period
The spatial distribution that the maximum wetland element of the domain extent of damage and wetland are damaged, determines the impaired mode of all subregion.
Those of ordinary skill in the art will appreciate that: realize that all or part of the steps of above method embodiment can pass through
The relevant hardware of program instruction is completed, and program above-mentioned can be stored in a computer readable storage medium, the program
When being executed, step including the steps of the foregoing method embodiments is executed；And storage medium above-mentioned includes: ROM, RAM, magnetic disk or light
The various media that can store program code such as disk.
The apparatus embodiments described above are merely exemplary, wherein described, unit can as illustrated by the separation member
It is physically separated with being or may not be, component shown as a unit may or may not be physics list
Member, it can it is in one place, or may be distributed over multiple network units.It can be selected according to the actual needs
In some or all of the modules achieve the purpose of the solution of this embodiment.Those of ordinary skill in the art are not paying creativeness
Labour in the case where, it can understand and implement.
Through the above description of the embodiments, those skilled in the art can be understood that each embodiment can
It realizes by means of software and necessary general hardware platform, naturally it is also possible to pass through hardware.Based on this understanding, on
Stating technical solution, substantially the part that contributes to existing technology can be embodied in the form of software products in other words, should
Computer software product may be stored in a computer readable storage medium, such as ROM/RAM, magnetic disk, CD, including several fingers
It enables and using so that a computer equipment (can be personal computer, server or the network equipment etc.) executes each implementation
Method described in certain parts of example or embodiment.
Finally, it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
Present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that: it still may be used
To modify the technical solutions described in the foregoing embodiments or equivalent replacement of some of the technical features；
And these are modified or replaceed, technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution spirit and
Range.
Claims (10)
1. a kind of wetland is damaged remote sensing recognition method characterized by comprising
The land use data for obtaining the first default historical time section region of interest within, according to the described first default historical time section
Interior land use data obtains the impaired spatial distribution and impaired type of Wetland Type data, wetland, according to the wetland class
Maximum wetland range in target area described in type data acquisition, and the maximum wetland range is divided into multiple subregions；
The image data for obtaining each subregion in the second default historical time section draws the described second default historical time section
It is divided into multiple sub- periods, according to the image data of all subregion in each sub- period, obtains in each sub- period
Multiple wetland elements of all subregion, and calculate each wetland element extent of damage of all subregion in each sub- period；
According to the impaired spatial distribution of the maximum wetland element of all subregion extent of damage, the wetland in each sub- period
With impaired type, the impaired mode of each subregion is determined.
2. the method according to claim 1, wherein according to the Wetland Type number in the land use data
According to, wetland impaired spatial distribution and impaired type, the step of obtaining the maximum wetland range in the target area, is specifically wrapped
It includes:
Obtain the land use data in the first default historical time section every preset duration；
By the mutation analysis to land use pattern, wetland impaired spatial distribution and impaired type are obtained；
To in each land use data Wetland Type data and the impaired spatial distribution of wetland and impaired type fold
Analysis is set, the maximum wetland range in the target area is obtained.
3. the method according to claim 1, wherein according to the image number of all subregion in each sub- period
According to the step of multiple wetland elements of all subregion specifically includes in acquisition each sub- period:
According to the image data of all subregion in each sub- period, wetland element index is obtained, extracts each son respectively
The normalization water body index of all subregion, normalized differential vegetation index and soil moisture index in period；
Using the normalization water body index of each subregion as the water body element of each subregion；
Using the normalized differential vegetation index of each subregion as the vegetation element of each subregion；
Using the soil moisture index of each subregion as the soil moisture element of each subregion.
4. according to the method described in claim 3, it is characterized in that, being calculated by the following formula wetland element index E:
E=F (NDWI, NDVI, SMMI)；
Wherein, the calculation formula of F function representation wetland element, NDWI, NDVI and SMMI indicate normalization water body index wetland water
Body, normalized differential vegetation index and soil moisture index；
It is calculated by the following formula the normalization water body index NDWI of all subregion in each sub- period:
It is calculated by the following formula the normalized differential vegetation index NDVI of all subregion in each sub- period:
It is calculated by the following formula the soil moisture index SMMI of all subregion in each sub- period:
Wherein, Green indicates that the green band in the image data, NIR indicate the near infrared band in the image data,
Red indicates that the red band in the image data, SWIR indicate the short infrared wave band in the image data.
5. the method according to claim 1, wherein being calculated by the following formula each son in each sub- period
Each wetland element extent of damage in region:
Wherein, Z indicates any wetland element saliency value of any subregion in any sub- period, and n indicates any described
The length of sub- period, EiIndicate any subregion 1 year in any sub- period any wetland element value, EjIt indicates
Any wetland element value in any subregion jth year in any sub- period；
According to the S value and Z value of each wetland element of all subregion in each sub- period, determine each in each sub- period
Each wetland element extent of damage of subregion.
6. according to the method described in claim 5, it is characterized in that, according to each wetland of all subregion in each sub- period
The S value and Z value of element, the step of determining each wetland element extent of damage of all subregion in each sub- period, specifically wrap
It includes:
It is corresponding with each default extent of damage default by the S value and Z value of each wetland element of all subregion in each period
Condition is compared；
The S value of each wetland element of all subregion and Z value are met in each period preset condition is corresponding default
Each wetland element extent of damage of the extent of damage as each subregion.
7. the method according to claim 1, wherein according to the Wetland Type number in the land use data
According to the step of obtaining the maximum wetland range in the target area further include:
With the land use data of finish time at the beginning of according to the described first default historical time section, land use is constructed
The transfer matrix of type；
According to the transfer matrix, various types of wetlands in the target area are determined；The type includes that can restore wetland
With irrecoverable wetland；
The wetland that restores includes that wetland is converted into farmland, wetland is converted into forest land and wetland is converted into meadow；
The irrecoverable wetland includes that wetland is converted into construction land；
Correspondingly, according to the maximum wetland element of all subregion extent of damage in each sub- period, each sub-district is determined
The step of impaired mode in domain, specifically includes:
According to the impaired sky of the wetland of the maximum wetland element of all subregion extent of damage and all subregion in each sub- period
Between be distributed, determine the impaired mode of each subregion.
8. a kind of wetland is damaged remote sensing recognition device characterized by comprising
Module is obtained, for obtaining the land use data of the first default historical time section region of interest within, according to described first
Land use data in default historical time section obtains the impaired spatial distribution and impaired type of Wetland Type data, wetland,
It is divided according to the maximum wetland range in target area described in the Wetland Type data acquisition, and by the maximum wetland range
For multiple subregions；
Computing module, it is pre- by described second for obtaining the image data of each subregion in the second default historical time section
If historical time section is divided into multiple sub- periods, according to the image data of all subregion in each sub- period, obtain each
Multiple wetland elements of all subregion in the sub- period, and each wetland for calculating all subregion in each sub- period is wanted
The plain extent of damage；
Identification module, for according to the maximum wetland element of all subregion extent of damage, the wetland in each sub- period
Impaired spatial distribution and impaired type, determines the impaired mode of each subregion.
9. a kind of electronic equipment characterized by comprising
At least one processor, at least one processor and bus；Wherein,
The processor and memory complete mutual communication by the bus；
The memory is stored with the program instruction that can be executed by the processor, and the processor calls described program to instruct energy
Enough methods executed as described in claim 1 to 7 is any.
10. a kind of non-transient computer readable storage medium, which is characterized in that the non-transient computer readable storage medium is deposited
Computer instruction is stored up, the computer instruction makes the computer execute the method as described in claim 1 to 7 is any.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811543634.6A CN109886067B (en) | 2018-12-17 | 2018-12-17 | Wetland damage remote sensing identification method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811543634.6A CN109886067B (en) | 2018-12-17 | 2018-12-17 | Wetland damage remote sensing identification method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109886067A true CN109886067A (en) | 2019-06-14 |
| CN109886067B CN109886067B (en) | 2021-05-14 |
Family
ID=66925114
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811543634.6A Active CN109886067B (en) | 2018-12-17 | 2018-12-17 | Wetland damage remote sensing identification method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109886067B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110298411A (en) * | 2019-07-04 | 2019-10-01 | 中国城市建设研究院有限公司 | A kind of impaired identification and evaluation method of group of cities ecological space |
| CN112801838A (en) * | 2020-12-25 | 2021-05-14 | 广州市城市规划勘测设计研究院 | Urban wetland ecological unit dividing method, device and storage medium thereof |
| CN112990657A (en) * | 2021-02-06 | 2021-06-18 | 首都师范大学 | Method for evaluating wetland degradation through long-time satellite remote sensing |
| CN117113160A (en) * | 2023-10-25 | 2023-11-24 | 北京大学深圳研究生院 | Post-disaster recovery condition monitoring method and device, computer equipment and storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106897668A (en) * | 2017-01-20 | 2017-06-27 | 浙江大学 | A kind of Grassland degradation degree extraction method based on remote sensing image |
| CN107271367A (en) * | 2017-05-05 | 2017-10-20 | 北京师范大学 | A kind of identifying water boy method and device |
| US20170316282A1 (en) * | 2016-04-27 | 2017-11-02 | SlantRange, Inc. | Systems and methods for object classification and selective compression in aerial imagery |
| CN107977765A (en) * | 2017-07-07 | 2018-05-01 | 江西省科学院 | A kind of lake and marshland Landscape health status evaluation method based on remote Sensing Interpretation technology |
| CN108629502A (en) * | 2018-05-02 | 2018-10-09 | 苏州农业职业技术学院 | A kind of Assessment of wetland ecosystem health method based on microbiological integrity index |
| CN108718932A (en) * | 2018-05-08 | 2018-11-02 | 南京林业大学 | A kind of seashore wetland method for recovering vegetation |
-
2018
- 2018-12-17 CN CN201811543634.6A patent/CN109886067B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170316282A1 (en) * | 2016-04-27 | 2017-11-02 | SlantRange, Inc. | Systems and methods for object classification and selective compression in aerial imagery |
| CN106897668A (en) * | 2017-01-20 | 2017-06-27 | 浙江大学 | A kind of Grassland degradation degree extraction method based on remote sensing image |
| CN107271367A (en) * | 2017-05-05 | 2017-10-20 | 北京师范大学 | A kind of identifying water boy method and device |
| CN107977765A (en) * | 2017-07-07 | 2018-05-01 | 江西省科学院 | A kind of lake and marshland Landscape health status evaluation method based on remote Sensing Interpretation technology |
| CN108629502A (en) * | 2018-05-02 | 2018-10-09 | 苏州农业职业技术学院 | A kind of Assessment of wetland ecosystem health method based on microbiological integrity index |
| CN108718932A (en) * | 2018-05-08 | 2018-11-02 | 南京林业大学 | A kind of seashore wetland method for recovering vegetation |
Non-Patent Citations (1)
| Title |
|---|
| 袁丽华 等: ""2000-2010年黄河流域植被覆盖的时空变化"", 《生态学报》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110298411A (en) * | 2019-07-04 | 2019-10-01 | 中国城市建设研究院有限公司 | A kind of impaired identification and evaluation method of group of cities ecological space |
| CN112801838A (en) * | 2020-12-25 | 2021-05-14 | 广州市城市规划勘测设计研究院 | Urban wetland ecological unit dividing method, device and storage medium thereof |
| CN112801838B (en) * | 2020-12-25 | 2023-06-16 | 广州市城市规划勘测设计研究院 | Urban wetland ecological unit division method and device and storage medium thereof |
| CN112990657A (en) * | 2021-02-06 | 2021-06-18 | 首都师范大学 | Method for evaluating wetland degradation through long-time satellite remote sensing |
| CN112990657B (en) * | 2021-02-06 | 2021-12-07 | 首都师范大学 | Method for evaluating wetland degradation through long-time satellite remote sensing |
| CN117113160A (en) * | 2023-10-25 | 2023-11-24 | 北京大学深圳研究生院 | Post-disaster recovery condition monitoring method and device, computer equipment and storage medium |
| CN117113160B (en) * | 2023-10-25 | 2024-02-02 | 北京大学深圳研究生院 | Post-disaster recovery condition monitoring method and device, computer equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109886067B (en) | 2021-05-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109886067A (en) | Wetland is damaged remote sensing recognition method and device | |
| Rußwurm et al. | Multi-temporal land cover classification with long short-term memory neural networks | |
| Oñate-Valdivieso et al. | Application of GIS and remote sensing techniques in generation of land use scenarios for hydrological modeling | |
| Masolele et al. | Spatial and temporal deep learning methods for deriving land-use following deforestation: A pan-tropical case study using Landsat time series | |
| Li et al. | Road network extraction via deep learning and line integral convolution | |
| EP3028240A1 (en) | Automatic generation of built-up layers from high resolution satellite image data | |
| CN108875487A (en) | Pedestrian is identified the training of network again and is identified again based on its pedestrian | |
| CN102651053A (en) | Evaluation method of debris flow disaster risk based on Bayesian network model | |
| CN109101938A (en) | A kind of multi-tag age estimation method based on convolutional neural networks | |
| Cheng et al. | Wheat yield estimation using remote sensing data based on machine learning approaches | |
| Mugiraneza et al. | Monitoring urbanization and environmental impact in Kigali, Rwanda using Sentinel-2 MSI data and ecosystem service bundles | |
| Yang et al. | Integrating multiple semantics data to assess the dynamic change of urban green space in Beijing, China | |
| Ma et al. | Using Bayesian optimization to automate the calibration of complex hydrological models: Framework and application | |
| CN111144168A (en) | Crop growth cycle identification method, equipment and system | |
| Xia et al. | Land resource use classification using deep learning in ecological remote sensing images | |
| Xiang et al. | Mapping potential wetlands by a new framework method using random forest algorithm and big Earth data: A case study in China's Yangtze River Basin | |
| CN107121681A (en) | Residential area extraction system based on high score satellite remote sensing date | |
| Ganjirad et al. | Google Earth Engine-based mapping of land use and land cover for weather forecast models using Landsat 8 imagery | |
| Yadav et al. | Computational model for urban growth using socioeconomic latent parameters | |
| Karmshahi et al. | Application of an integrated CA-Markov model in simulating spatiotemporal changes in forest cover: a case study of Malekshahi county forests, Ilam province | |
| Qian et al. | Deep Learning-based Near-real-time Monitoring of Autumn Irrigation Extent at Sub-pixel Scale in a large Irrigation Districth | |
| CN108985493A (en) | A kind of ground class variation prediction method based on self-adapting changeable filter | |
| Vu et al. | Streamflow prediction in the Mekong River Basin using deep neural networks | |
| CN106372765A (en) | Method for restoring data of highway meteorological station based on data integration | |
| Deng et al. | A paddy field segmentation method combining attention mechanism and adaptive feature fusion |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |